Smart Autism Spectrum Disorder Learning System Based on Remote Edge Healthcare Clinics and Internet of Medical Things.

Autism spectrum disorder (ASD) is a brain disorder causing issues among many young children. For children suffering from ASD, their learning ability is typically slower when compared to normal children. Therefore, many technologies aiming to teach ASD children with optimized learning approaches have emerged.

Anti-hepatocellular carcinoma activities of novel hydrazone derivatives downregulation of interleukin-6.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related morbidity worldwide. Sorafenib is a first-line drug for the treatment of HCC, however, it is reported to cause serious adverse effects and may lead to resistance in many patients. In this study, 20 hydrazone derivatives incorporating triazoles, pyrazolone, pyrrole, pyrrolidine, imidazoline, quinazoline, and oxadiazine moieties were designed, synthesized, and characterized.

Digital healthcare framework for patients with disabilities based on deep federated learning schemes.

Utilizing digital healthcare services for patients who use wheelchairs is a vital and effective means to enhance their healthcare. Digital healthcare integrates various healthcare facilities, including local laboratories and centralized hospitals, to provide healthcare services for individuals in wheelchairs. In digital healthcare, the Internet of Medical Things (IoMT) allows local wheelchairs to connect with remote digital healthcare services and generate sensors from wheelchairs to monitor and process healthcare.

Intriguing and Facile Preparation Approach of CdO Nanorod-Based Abundant Chitosan for Symmetric Supercapacitors.

Abundant chitosan was rationally used for the green fabrication of cadmium oxide nanorods (CdO nanorods) owing to its environmentally benign characteristics, bioavailability, low cost, etc. However, the primary unsubstituted amino group of chitosan interacts with the surface of Cd salt at higher temperatures, resulting in CdO nanorod formation. A one-step hydrothermal technique was adopted in the presence of chitosan.

Autism Spectrum Disorder detection framework for children based on federated learning integrated CNN-LSTM.

The incidence of Autism Spectrum Disorder (ASD) among children, attributed to genetics and environmental factors, has been increasing daily. ASD is a non-curable neurodevelopmental disorder that affects children's communication, behavior, social interaction, and learning skills. While machine learning has been employed for ASD detection in children, existing ASD frameworks offer limited services to monitor and improve the health of ASD patients.

Impact of Bonding Temperature on Microstructure, Mechanical, and Fracture Behaviors of TLP Bonded Joints of Al2219 with a Cu Interlayer.

The present study aims at producing transient liquid phase (TLP) bonded Al2219 joints with pure Cu (copper) as an interlayer. The TLP bonding is carried out at the bonding temperatures in the range of 480 to 520 °C while keeping the bonding pressure (2 MPa) and time (30 min.) constant.

Impact of Boron Carbide Particles and Weight Percentage on the Mechanical and Wear Characterization of Al2011 Alloy Metal Composites.

Micron-sized BC addition to the Al2011 alloy was investigated for its impact on mechanical and wear performance. The stir-casting method was used to manufacture the Al2011 alloy metal matrix composites reinforced with varying percentages of BC particulates (2, 4, and 6). The microstructural, mechanical, and wear properties of the synthesized composites were tested.

An IoT Enable Anomaly Detection System for Smart City Surveillance.

Since the advent of visual sensors, smart cities have generated massive surveillance video data, which can be intelligently inspected to detect anomalies. Computer vision-based automated anomaly detection techniques replace human intervention to secure video surveillance applications in place from traditional video surveillance systems that rely on human involvement for anomaly detection, which is tedious and inaccurate. Due to the diverse nature of anomalous events and their complexity, it is however, very challenging to detect them automatically in a real-world scenario.

Solar Power Prediction Using Dual Stream CNN-LSTM Architecture.

The integration of solar energy with a power system brings great economic and environmental benefits. However, the high penetration of solar power is challenging due to the operation and planning of the existing power system owing to the intermittence and randomicity of solar power generation. Achieving accurate predictions for power generation is important to provide high-quality electric energy for end-users.

An Efficient Lightweight Hybrid Model with Attention Mechanism for Enhancer Sequence Recognition.

Enhancers are sequences with short motifs that exhibit high positional variability and free scattering properties. Identification of these noncoding DNA fragments and their strength are extremely important because they play a key role in controlling gene regulation on a cellular basis. The identification of enhancers is more complex than that of other factors in the genome because they are freely scattered, and their location varies widely.

An Efficient Pest Detection Framework with a Medium-Scale Benchmark to Increase the Agricultural Productivity.

Insect pests and crop diseases are considered the major problems for agricultural production, due to the severity and extent of their occurrence causing significant crop losses. To increase agricultural production, it is significant to protect the crop from harmful pests which is possible via soft computing techniques. The soft computing techniques are based on traditional machine and deep learning-based approaches.

Biological evaluation, docking studies, and ADME prediction of some pyrimidine and pyridine derivatives as potential EGFR and EGFR inhibitors.

Herein, a set of pyridine and pyrimidine derivatives were assessed for their impact on the cell cycle and apoptosis. Human breast cancer (MCF7), hepatocellular carcinoma (HEPG2), larynx cancer (HEP2), lung cancer (H460), colon cancers (HCT116 and Caco2), and hypopharyngeal cancer (FADU), and normal Vero cell lines were used. Compounds and displayed outstanding effects on the investigated cell lines and were further tested for their antioxidant activity in MCF7, H460, FADU, HEP2, HEPG2, HCT116, Caco2, and Vero cells by measuring superoxide dismutase (SOD), malondialdehyde content (MDA), reduced glutathione (GSH), and nitric oxide (NO) content.

Improving the Efficiency of Multistep Short-Term Electricity Load Forecasting via R-CNN with ML-LSTM.

Multistep power consumption forecasting is smart grid electricity management's most decisive problem. Moreover, it is vital to develop operational strategies for electricity management systems in smart cities for commercial and residential users. However, an efficient electricity load forecasting model is required for accurate electric power management in an intelligent grid, leading to customer financial benefits.

Method for Determining Treated Metal Surface Quality Using Computer Vision Technology.

Computer vision and image processing techniques have been extensively used in various fields and a wide range of applications, as well as recently in surface treatment to determine the quality of metal processing. Accordingly, digital image evaluation and processing are carried out to perform image segmentation, identification, and classification to ensure the quality of metal surfaces. In this work, a novel method is developed to effectively determine the quality of metal surface processing using computer vision techniques in real time, according to the average size of irregularities and caverns of captured metal surface images.

Enhanced Anomaly Detection System for IoT Based on Improved Dynamic SBPSO.

The Internet of Things (IoT) supports human endeavors by creating smart environments. Although the IoT has enabled many human comforts and enhanced business opportunities, it has also opened the door to intruders or attackers who can exploit the technology, either through attacks or by eluding it. Hence, security and privacy are the key concerns for IoT networks.

A Deep-Learning Model for Real-Time Red Palm Weevil Detection and Localization.

Over the last two decades, particularly in the Middle East, Red Palm Weevils (RPW, Rhynchophorus ferruginous) have proved to be the most destructive pest of palm trees across the globe. The RPW has caused considerable damage to various palm species. The early identification of the RPW is a challenging task for good date production since the identification will prevent palm trees from being affected by the RPW.

An Effective Skin Cancer Classification Mechanism via Medical Vision Transformer.

Skin Cancer (SC) is considered the deadliest disease in the world, killing thousands of people every year. Early SC detection can increase the survival rate for patients up to 70%, hence it is highly recommended that regular head-to-toe skin examinations are conducted to determine whether there are any signs or symptoms of SC. The use of Machine Learning (ML)-based methods is having a significant impact on the classification and detection of SC diseases.

To Assist Oncologists: An Efficient Machine Learning-Based Approach for Anti-Cancer Peptides Classification.

In the modern technological era, Anti-cancer peptides (ACPs) have been considered a promising cancer treatment. It's critical to find new ACPs to ensure a better knowledge of their functioning processes and vaccine development. Thus, timely and efficient ACPs using a computational technique are highly needed because of the enormous peptide sequences generated in the post-genomic era.

An Efficient and Effective Deep Learning-Based Model for Real-Time Face Mask Detection.

Since December 2019, the COVID-19 pandemic has led to a dramatic loss of human lives and caused severe economic crises worldwide. COVID-19 virus transmission generally occurs through a small respiratory droplet ejected from the mouth or nose of an infected person to another person. To reduce and prevent the spread of COVID-19 transmission, the World Health Organization (WHO) advises the public to wear face masks as one of the most practical and effective prevention methods.

RF Remote Blood Glucose Sensor and a Microfluidic Vascular Phantom for Sensor Validation.

Diabetes has become a major health problem in society. Invasive glucometers, although precise, only provide discrete measurements at specific times and are unsuitable for long-term monitoring due to the injuries caused on skin and the prohibitive cost of disposables. Remote, continuous, self-monitoring of blood sugar levels allows for active and better management of diabetics.